What if machines could discover human behavioral patterns better than experts? Traditional behavioral modeling in economics depends on costly manual refinement by domain experts, severely limiting scalability and discovery potential. We introduce AutoBM, an automated behavioral modeling framework leveraging large language models (LLMs) to systematically generate, evaluate, and refine interpretable behavioral models directly from human behavior data. AutoBM represents candidate models as structured natural language specifications, explicitly defining symbolic terms along with their tunable parameters, interpretations, and design rationales. AutoBM leverages LLMs to automatically translate each language specification into executable code, optimize tunable parameters, and evaluate model performance. Utilizing LLM-guided search strategies, AutoBM iteratively recombines and improves models at the term level, closely mirroring human expert practices. Experiments conducted across three distinct strategic environments (the ultimatum game, repeated rock-paper-scissors, and continuous double auctions) demonstrate that AutoBM-generated models consistently outperform leading manually crafted models, achieving significant improvements in prediction accuracy while maintaining clear interpretability. Our results demonstrate that automated frameworks can not only match but systematically exceed human expertise in behavioral modeling, fundamentally changing how we understand strategic human behavior.

The strategic behavior of users is significantly influenced by their hidden information such as private valuations, risk preferences, and price sensitivities. Contextual behavioral model learning refers to learning the dependence of users' hidden information on their observable context information. While many existing studies use offline data to learn contextual behavioral models, we study how to design sequential experiments to collect the most informative user behavioral data for learning. We propose a basic inference-then-design method. In each experimental period, it infers a probabilistic contextual behavioral model using historical experimental data, and then designs the new experiment to maximize the gain of information about the probabilistic model. We further improve the basic method in two aspects. First, we improve the inference step by specifying a more informative prior for learning the probabilistic contextual behavioral model. Second, we integrate the inference and design steps instead of conducting them separately. Our rigorous theoretic analysis reveals that the optimization objective of the inference step can be modified to account for the downstream experimental design step. Numerical experiments show that our methods lead to more effective experiments, i.e., the collected experimental data can help in learning a more accurate behavioral model.

Estimating player utilities from observed equilibria is crucial for many applications. Existing approaches to tackle this problem are either limited to specific games or do not scale well with the number of players. Our work addresses these issues by proposing a novel utility estimation method for general multi-player non-cooperative games. Our main idea consists in reformulating the inverse game problem as an inverse variational inequality problem and in selecting among all utility parameters consistent with the data, the so-called incenter. We show that the choice of the incenter can produce parameters that are most robust to the observed equilibrium behaviors. However, its computation is challenging, as the number of constraints in the corresponding optimization problem increases with the number of players and the behavior space size. To tackle this challenge, we propose a loss function-based algorithm, making our method scalable to games with many players or a continuous action space. Furthermore, we show that our method can be extended to incorporate prior knowledge of player utilities, and that it can handle inconsistent data, i.e., data where players do not play exact equilibria. Numerical experiments on three game applications demonstrate that our methods outperform the state of the art. The code, datasets, and supplementary material are available at https://github.com/cuilvye/Incenter-Project.

Inferring the private information of humans from their strategic behavioral data is crucial and challenging. The main approach is first obtaining human behavior functions (which map public information and human private information to behavior), enabling subsequent inference of private information from observed behavior. Most existing studies rely on strong equilibrium assumptions to obtain behavior functions. Our work focuses on continuous double auctions, where multiple traders with heterogeneous rationalities and beliefs dynamically trade commodities and deriving equilibria is generally intractable. We develop a knowledge-aware machine learning-based framework to infer each trader's private cost vectors for producing different units of its commodity. Our key idea is to learn behavior functions by incorporating the statistical knowledge about private costs given the observed trader asking behavior across the population. Specifically, we first use a neural network to characterize each trader's behavior function. Second, we leverage the statistical knowledge to derive the posterior distribution of each trader's private costs given its observed asks. Third, through designing a novel loss function, we utilize the knowledge-based posterior distributions to guide the learning of the neural network. We conduct extensive experiments on a large experimental dataset, and demonstrate the superior performance of our framework over baselines in inferring the private information of humans.

Game theory and machine learning are two widely used techniques for predicting the outcomes of strategic interactions among humans. However, the game theory-based approach often relies on strong rationality and informational assumptions, while the machine learning-based approach typically requires the testing data to come from the same distribution as the training data. Our work studies how to integrate the two techniques to address these weaknesses. We focus on the interactions among real bidders in penny auctions, and develop a three-stage framework to predict the distributions of auction durations, which indicate the numbers of bids and auctioneer revenues. Specifically, we first leverage a pre-trained neural network to encode the descriptions of products in auctions into embeddings. Second, we apply game theory models to make preliminary predictions of auction durations. In particular, we tackle the challenge of accurately inferring parameters in game theory models. Third, we develop a Multi-Branch Mixture Density Network to learn the mapping from product embeddings and game-theoretic predictions to the distributions of actual auction durations. Experiments on real-world penny auction data demonstrate that our framework outperforms both game theory-based and machine learning-based prediction approaches.

There have been many studies leveraging bargaining to incentivize the sharing of network resources between resource owners and seekers. They predicted bargaining behavior and outcomes mainly by assuming that bargainers are fully rational and possess sufficient knowledge about their opponents. Our work addresses the prediction of bargaining behavior in network resource sharing scenarios where these assumptions do not hold, i.e., bargainers are bounded-rational and have heterogeneous knowledge. Our first key idea is using a multi-output Long Short-Term Memory (LSTM) neural network to learn bargainers’ behavior patterns and predict both their discrete and continuous decisions. Our second key idea is assigning a unique latent vector to each bargainer, characterizing the heterogeneity among bargainers. We propose a scheme to jointly learn the LSTM weights and latent vectors from real bargaining data, and utilize them to achieve a personalized behavior prediction. We prove that estimating our LSTM weights corresponds to a special design of LSTM training, and also theoretically characterize the performance of our scheme. To deal with large-scale datasets in practice, we further propose a variant of our scheme to accelerate the LSTM training. Experiments on a large real-world bargaining dataset demonstrate that our schemes achieve more accurate personalized predictions than baselines.

Inferring bargainers' private valuations on items from their decisions is crucial for analyzing their strategic behaviors in bilateral sequential bargaining. Most existing approaches that infer agents' private information from observable data either rely on strong equilibrium assumptions or require a careful design of agents' behavior models. To overcome these weaknesses, we propose a Bayesian Learning-based Valuation Inference (BLUE) framework. Our key idea is to derive feasible intervals of bargainers' private valuations from their behavior data, using the fact that most bargainers do not choose strictly dominated strategies. We leverage these feasible intervals to guide our inference. Specifically, we first model each bargainer's behavior function (which maps his valuation and bargaining history to decisions) via a recurrent neural network. Second, we learn these behavior functions by utilizing a novel loss function defined based on feasible intervals. Third, we derive the posterior distributions of bargainers' valuations according to their behavior data and learned behavior functions. Moreover, we account for the heterogeneity of bargainer behaviors, and propose a clustering algorithm (K-Loss) to improve the efficiency of learning these behaviors. Experiments on both synthetic and real bargaining data show that our inference approach outperforms baselines.

We study the design of incentive mechanisms for the problem of information elicitation without verification (IEWV). In IEWV, a data requester seeks to design proper incentives to optimize the tradeoff between the quality of information (collected from distributed crowd workers) and the total cost of incentives (provided to crowd workers) without verifiable ground truth. While prior work often relies on sufficient knowledge of worker information, we study a scenario where the data requester cannot access workers’ heterogeneous information quality and costs ex-ante. We propose a continuum-armed bandit-based incentive mechanism that dynamically learns the optimal reward level from workers’ reported information. A key challenge is that the data requester cannot evaluate the workers’ information quality without verification, which motivates the design of an inference algorithm. The inference problem is non-convex, yet we reformulate it as a bi-convex problem and derive an approximate solution with a performance guarantee, which ensures the effectiveness of our online reward design. We further enhance the inference algorithm using part of the workers’ historical reports. We also propose a novel rule for the data requester to aggregate workers’ solutions more effectively. We show that our mechanism achieves a sub-linear regret$\tilde {O}(T^{1/2})$and outperforms several celebrated benchmarks.

We study a crowdsourcing problem, where a platform aims to incentivize distributed workers to provide high-quality and truthful solutions that are not verifiable. We focus on a largely overlooked yet pratically important asymmetric information scenario, where the platform knows more information regarding workers’ average solution accuracy and can strategically reveal such information to workers. Workers will utilize the announced information to determine the likelihood of obtaining a reward. We first study the case where the platform and workers share the same prior regarding the average worker accuracy (but only the platform observes the realized value). We consider two types of workers: (1)naiveworkers who fully trust the platform's announcement, and (2)strategicworkers who update prior belief based on the announcement. For naive workers, we show that the platform should always announce a high average accuracy to maximize its payoff. However, this is not always optimal when facing strategic workers, and the platform may benefit from announcing an average accuracy lower than the actual value. We further study the more challenging non-common prior case, and show the counter-intuitive result that when the platform is uninformed of the workers’ prior, both the platform payoff and the social welfare may decrease as the high accuracy workers’ solutions become more accurate.

When it is difficult to verify contributed solutions in mobile crowdsourcing, the majority voting mechanism is widely utilized to incentivize distributed workers to provide high-quality and truthful solutions. In the majority voting mechanism, a worker is rewarded based on whether his solution is consistent with the majority. However, most prior related work relies on a strong assumption that workers solution accuracy levels are public knowledge, which may not hold in many practical scenarios. We relax such an assumption and propose an online mechanism, which allows the platform to learn the distribution of the workers solution accuracy levels via asking workers to report their private accuracy levels (which do not need to be the true values), in addition to deciding their effort levels and solution reporting strategies. The mechanism design is challenging, as neither the workers task solutions nor their accuracy reports can be verified. We devise a randomized reward mechanism that computes the workers rewards based on their reported accuracy levels, under which the workers obtain rewards if their reported solutions match the majority. Our mechanism induces workers to truthfully report their solution accuracy levels in the long run, and the empirical accuracy distribution converges to the actual accuracy distribution.

Mobile crowdsourcing platforms often want to incentivize workers to finish tasks with high quality and truthfully report their solutions by providing proper rewards. Most existing incentive mechanisms reward workers based on the comparison among workers’ reported solutions. However, these mechanisms are vulnerable to worker collusion, i.e., workers coordinate to misreport their solutions. We address such an issue by proposing a novel rewarding mechanism based on a${truth detection}$technology, which relies on the independent verification of the correctness of each worker’s response to some question with animperfectaccuracy. We model the interactions between the platform and workers as a two-stage Stackelberg game. In Stage I, the platform optimizes the reward mechanism parameters associated withtruth detectionto maximize its payoff. In Stage II, the workers decide their effort levels and reporting strategies to maximize their payoffs (which depend on the output of the truth detector). We analyze the game’s equilibrium and show that our proposed mechanism can effectively mitigate worker collusion. We also propose a novel rule, namedfiltered majority, for the platform to more effectively aggregate the workers’ solutions. Our proposed aggregation rule utilizes truth detection and outperforms the conventional simple majority rule. We further characterize the impact of the truth detection accuracy on the platform’s decisions. Surprisingly, under the simple majority rule, we show that as the truth detection accuracy improves, the platform should always incentivize more workers to exert effort and truthfully report. However, under our proposed filtered majority rule, we show that as the truth detection accuracy improves, in some cases, the platform should incentivize fewer workers and save costs. We further examine the impact of the workers’ imperfect estimation of the truth detection accuracy on the platform’s decisions.

In mobile crowdsourcing, platforms seek to incentivize heterogeneous workers to complete tasks (e.g., road traffic sensing) and truthfully report their solutions. When platforms cannot verify the quality of the workers’ solutions, the crowdsourcing problem is known asinformation elicitation without verification(IEWV). In an IEWV problem, a platform needs to provide incentives to motivate high-quality solutions and truthful reporting of the solutions from the workers. A common approach to solve the IEWV problem is majority voting, where each worker is rewarded according to whether his solution matches the majority’s solution. However, previous work has not considered workers with heterogeneous solution accuracy. This is unrealistic in many domains, where one would expect workers to differ in judgment, expertise, and reliability. Moreover, prior work has not considered how this heterogeneity affects a platform’s tradeoff between the quality of the workers’ solutions and the platform’s cost of achieving this. We address these gaps by studying the interactions between the mobile crowdsourcing platform and workers as a two-stage Stackelberg game. In Stage I, the platform chooses the reward level for majority voting. In Stage II, the workers decide their effort levels and reporting strategies. We show that as a worker’s solution accuracy increases, he is more likely, in equilibrium, to exert effort and truthfully report his solution. However, given a fixed total worker population, surprisingly, the platform’s payoff may decrease in the number of high-accuracy workers. We further characterize the value of knowing the workers’ solution accuracy in terms of improving the platform’s optimal reward design and maximizing its payoff. Knowing such information enables a more effective aggregation of the workers’ solutions. We further design a discriminatory reward policy to incentivize heterogeneous workers. Surprisingly, such a discriminatory policy can improve both the platform’s and the workers’ payoffs, and hence improve the social welfare.

We study a crowdsourcing problem where the platform aims to incentivize distributed workers to provide high-quality and truthful solutions without the ability to verify the solutions. While most prior work assumes that the platform and workers have symmetric information, we study an asymmetric information scenario where the platform has informational advantages. Specifically, the platform knows more information regarding workers' average solution accuracy, and can strategically reveal such information to workers. Workers will utilize the announced information to determine the likelihood that they obtain a reward if exerting effort on the task. We study two types of workers: (1) naive workers who fully trust the announcement, and (2) strategic workers who update prior belief based on the announcement. For naive workers, we show that the platform should always announce a high average accuracy to maximize its payoff. However, this is not always optimal for strategic workers, as it may reduce the credibility of the platform's announcement and hence reduce the platform's payoff. Interestingly, the platform may have an incentive to even announce an average accuracy lower than the actual value when facing strategic workers. Another counter-intuitive result is that the platform's payoff may decrease in the number of high-accuracy workers.

It can be profitable for vehicle service providers to set service prices based on users' travel demand on different origin-destination pairs. Prior studies on the spatial pricing of vehicle service rely on the assumption that providers know users' demand. In this paper, we study a monopolistic provider who initially does not know users' demand and needs to learn it over time by observing the users' responses to the service prices. We design a pricing and vehicle supply policy, considering the tradeoff between exploration (i.e., learning the demand) and exploitation (i.e., maximizing the provider's short-term payoff). Considering that the provider needs to ensure the vehicle flow balance at each location, its pricing and supply decisions for different origin-destination pairs are tightly coupled. This makes it challenging to theoretically analyze the performance of our policy. We analyze the gap between the provider's expected time-average payoffs under our policy and a clairvoyant policy, which makes decisions based on complete information of the demand. We prove that after running our policy for D days, the loss in the expected time-average payoff can be at most O((ln D)1/2D−1/4), which decays to zero as D approaches infinity.

Most mobile network operators generate revenues by directly charging users for data plan subscriptions. Some operators now also offer users data rewards to incentivize them to watch mobile ads, which enables the operators to collect payments from advertisers and create new revenue streams. In this work, we analyze and compare two data rewarding schemes: a Subscription-Aware Rewarding (SAR) scheme and a Subscription-Unaware Rewarding (SUR) scheme. Under the SAR scheme, only the subscribers of the operators' data plans are eligible for the rewards; under the SUR scheme, all users are eligible for the rewards (e.g., the users who do not subscribe to the data plans can still get SIM cards and receive data rewards by watching ads). We model the interactions among an operator, users, and advertisers by a two-stage Stackelberg game, and characterize their equilibrium strategies under both the SAR and SUR schemes. We show that the SAR scheme can lead to more subscriptions and a higher operator revenue from the data market, while the SUR scheme can lead to better ad viewership and a higher operator revenue from the ad market. We further show that the operator's optimal choice between the two schemes is sensitive to the users' data consumption utility function and the operator's network capacity. We provide some counter-intuitive insights. For example, when each user has a logarithmic utility function, the operator should apply the SUR scheme (i.e., reward both subscribers and non-subscribers) if and only if it has a small network capacity.

An increasing number of mobile applications (abbrev. apps), like Pokemon Go and Snapchat, reward the users who physically visit some locations tagged as POIs (places-of-interest) by the apps. We study the novel POI-based collaboration between apps and venues (e.g., restaurants). On the one hand, an app charges a venue and tags the venue as a POI. The POI tag motivates users to visit the venue, which potentially increases the venue's sales. On the other hand, the venue can invest in the app-related infrastructure, which enables more users to use the app and further benefits the app's business. The apps' existing POI tariffs cannot fully incentivize the venue's infrastructure investment, and hence cannot lead to the most effective app-venue collaboration. We design an optimal two-part tariff, which charges the venue for becoming a POI, and subsidizes the venue every time a user interacts with the POI. The subsidy design efficiently incentivizes the venue's infrastructure investment, and we prove that our tariff achieves the highest app's revenue among a general class of tariffs. Furthermore, we derive some counter-intuitive guidelines for the POI-based collaboration. For example, a bandwidth-consuming app should collaborate with a low-quality venue (users have low utilities when consuming the venue's products).

Many online social networking platforms are leveraging crowdsourcing to enhance the user experience. These platforms seek to incentivize heterogeneous workers to exert efforts to complete tasks (e.g., moderation of posts and articles) and truthfully report their solutions. Output agreement mechanism (e.g., majority voting) is a common approach to this end. In an output agreement mechanism, a worker is rewarded according to whether his solution matches those of his peers. However, prior related work has not studied the workers' heterogeneous solution accuracy and how this heterogeneity affects the platform's payoff. We fill this void by modeling and analyzing the interactions between the platform and workers as a two-stage Stackelberg game. In Stage I, the platform chooses the reward level for the majority voting to maximize its payoff. In Stage II, the workers decide their effort levels and reporting strategies to maximize their payoffs. We show that as a worker's solution accuracy increases, he is more likely to exert effort and truthfully report his solution under the equilibrium reward mechanism. However, given a fixed total worker population, it is surprising that the platform's overall payoff does not monotonically increase in the number of high-accuracy workers. This is because a larger number of high-accuracy workers brings marginally decreasing benefit to the platform, but the rewards required to incentivize them may significantly grow. Moreover, we show that as the solutions of the high-accuracy workers become more accurate, the platform needs a smaller number of such workers to achieve the maximum payoff.

Conventionally, mobile network operators charge users for data plan subscriptions. To create new revenue streams, some operators now also incentivize users to watch ads with data rewards and collect payments from advertisers. In this work, we study two such rewarding schemes: a Subscription-Aware Rewarding (SAR) scheme and a Subscription-Unaware Rewarding (SUR) scheme. Under the SAR scheme, only the subscribers of the operators' existing data plans are eligible for the rewards; under the SUR scheme, all users are eligible for the rewards (e.g., the users who do not subscribe to the data plans can still get SIM cards and receive data rewards by watching ads). We model the interactions among a capacity-constrained operator, users, and advertisers by a two-stage Stackelberg game, and characterize their equilibrium strategies under both the SAR and SUR schemes. We show that the SAR scheme can lead to more subscriptions and a higher operator revenue from the data market, while the SUR scheme can lead to better ad viewership and a higher operator revenue from the ad market. We provide some counter-intuitive insights for the design of data rewards. For example, the operator's optimal choice between the two schemes is sensitive to the users' data consumption utility function. When each user has a logarithmic utility function, the operator should apply the SUR scheme (i.e., reward both subscribers and nonsubscribers) if and only if it has a small network capacity.

We analyze the prioritized sharing between an added value Mobile Virtual Network Operator (MVNO) and multiple Mobile Network Operators (MNOs). An added value MVNO is one which earns added revenue from wireless users in addition to the revenue it directly collects for providing them wireless service. To offer service, an MVNO needs to contract with one or more MNOs to utilize their networks. Agreeing on such a contract requires the MNOs to consider the impact on their revenue from allowing the MVNO to enter the market as well as the possibility that other MNOs will cooperate. To further protect their customers, the MNOs may prioritize their direct customers over those of the MVNO. We establish a multi-stage game to analyze the equilibrium decisions of the MVNO, MNOs, and users in such a setting. In particular, we characterize the condition under which the MVNO can collaborate with the MNOs. The results show that the MVNO tends to cooperate with the MNOs when the band resources are limited and the added value is significant. When there is significant difference in band resources among the MNOs, the MVNO first considers cooperating with the MNO with a smaller band. We also consider the case when the users also have access to unlicensed spectrum.

Mobile Virtual Network Operators (MVNOs) are an increasingly growing segment of the market for wireless services. MVNOs do not own their own network infrastructure and so must cooperate with existing Mobile Network Operators (MNOs) to gain access to the network infrastructure needed to enter this market. Cooperating with an MVNO is a non-trivial decision for an MNO in part because the MVNO may then become a potential competitor for customers. One motive for entering into such an arrangement is that the MVNO receives an added value from serving customers beyond what it earns from charging them for wireless service. We study a game theoretic model for the cooperation and competition between an MNO and such an added value MVNO based on models for price competition with congestible resources. Our model captures two different dimensions of how an MNO may cooperate. The first dimension is the payment scheme between the MNO and the MVNO. The second dimension is the access priority that the MNO chooses to offer to the MVNO's customers. We characterize the pros and cons of different cooperation modes and analyze the optimal cooperation mode under different conditions.

Many mobile applications (abbrev. apps) reward the users who physically visit some locations tagged as POIs (places-of-interest) by the apps. In this paper, we study the POI-based collaboration between apps and venues (e.g., restaurants and cafes). On the one hand, an app charges a venue and tags the venue as a POI, which attracts users to visit the venue and potentially increases the venue's sales. On the other hand, the venue can invest in the app-related infrastructure (e.g., Wi-Fi networks and smartphone chargers), which enhances the users' experience of using the app. However, the existing POI pricing schemes of the apps (e.g., Pokemon Go and Snapchat) cannot incentivize the venue's infrastructure investment, and hence cannot achieve the most effective app-venue collaboration. We model the interactions among an app, a venue, and users by a three-stage Stackelberg game, and design an optimal two-part pricing scheme for the app. This scheme has a charge-with-subsidy structure: the app first charges the venue for becoming a POI, and then subsidizes the venue every time a user interacts with the POI. Compared with the existing pricing schemes, our two-part pricing better incentivizes the venue's investment, attracts more users to interact with the POI, and achieves a much larger app revenue. We analyze the impacts of the app's and venue's characteristics on the app's optimal revenrevenueue, and show that the apps with small and large congestion effects should collaborate with opposite types of venues.

Motivated by the recent efforts in extending long term evolution (LTE) to the unlicensed spectrum, we propose a novel spectrum sharing framework for the coopetition (i.e., cooperation and competition) between LTE and Wi-Fi in the unlicensed band. Basically, the LTE network can choose to work in one of the two modes: in the competition mode, it randomly accesses an unlicensed channel, and interferes with the Wi-Fi access point using the same channel; in the cooperation mode, it onloads the Wi-Fi users' traffic in exchange for the exclusive access of the corresponding channel. We design a second-price reverse auction mechanism, which enables the LTE provider and the Wi-Fi access point owners (APOs) to effectively negotiate the operation mode. Specifically, the LTE provider is the auctioneer (buyer), and the APOs are the bidders (sellers) who compete to sell the rights of onloading the APOs' traffic to the LTE provider. In Stage I of the auction, the LTE provider announces a reserve rate, which is the maximum data rate that it is willing to allocate to the APOs in the cooperation mode. In Stage II of the auction, the APOs submit their bids, which indicate the data rates that they would like the LTE provider to offer in the cooperation mode. We show that the auction involves allocative externalities, i.e., the cooperation between the LTE provider and one APO benefits other APOs who are not directly involved in this cooperation. We characterize the APOs' unique equilibrium bidding strategies in Stage II, and analyze the LTE provider's optimal reserve rate in Stage I. Numerical results show that our framework improves the payoffs of both the LTE provider and the APOs comparing with a benchmark scheme. In particular, our framework increases the LTE provider's payoff by 70% on average, when the LTE provider has a large throughput and a small data rate discounting factor. Moreover, our framework leads to a close-to-optimal social welfare under a large LTE throughput.

We study the cooperation of the mobile network operator (MNO) and the venue owners (VOs) on the public Wi-Fi deployment. We consider aone-to-many bargainingframework, where the MNO bargains with VOs sequentially to determine where to deploy Wi-Fi and how much to pay. Taking into account the negative externalities among different steps of bargaining, we analyze the following two cases: for theexogenous bargaining sequencecase, we compute the optimal bargaining solution on the cooperation decisions and payments under a predetermined bargaining sequence; for theendogenous bargaining sequencecase, the MNO decides the bargaining sequence to maximize its payoff. Through exploring the structural property of the optimal bargaining sequence, we design a low-complexityOptimal VO Bargaining Sequencing(OVBS) algorithm to search the optimal sequence. More specifically, we categorize the VOs into three types based on the impact of the Wi-Fi deployment at their venues, and show that it is optimal for the MNO to bargain with these three types of VOs sequentially. Numerical results show that compared with the random and worst bargaining sequences, the optimal bargaining sequence improves the MNO's payoff by up to 14.8 and 45.3 percent, respectively.

The proliferation of public Wi-Fi hotspots has brought new business potentials for Wi-Fi networks, which carry a significant amount of global mobile data traffic today. In this paper, we propose a novelWi-Fi monetizationmodel for venue owners (VOs) deploying public Wi-Fi hotspots, where the VOs can generate revenue by providing two different Wi-Fi access schemes for mobile users (MUs): 1) thepremium access, in which MUs directly pay VOs for their Wi-Fi usage, and 2) theadvertising sponsored access, in which MUs watch advertisements in exchange of the free usage of Wi-Fi. VOs sell their ad spaces to advertisers (ADs) via an ad platform, and share the ADs’ payments with the ad platform. We formulate the economic interactions among the ad platform, VOs, MUs, and ADs as a three-stage Stackelberg game. In Stage I, the ad platform announces its advertising revenue sharing policy. In Stage II, VOs determine the Wi-Fi prices (for MUs) and advertising prices (for ADs). In Stage III, MUs make access choices and ADs purchase advertising spaces. We analyze the sub-game perfect equilibrium (SPE) of the proposed game systematically, and our analysis shows the following useful observations. First, the ad platform’s advertising revenue sharing policy in Stage I will affect only the VOs’ Wi-Fi prices but not the VOs’ advertising prices in Stage II. Second, both the VOs’ Wi-Fi prices and advertising prices are non-decreasing in the advertising concentration level and non-increasing in the MU visiting frequency. Numerical results further show that the VOs are capable of generating large revenues through mainly providing one type of Wi-Fi access (the premium access or advertising sponsored access), depending on their advertising concentration levels and MU visiting frequencies.

There has been a proliferation of public Wi-Fi hotspots that serve a significant amount of global mobile traffic today. In this paper, we propose a general Wi-Fi monetization model for public Wi-Fi hotspots deployed by venue owners (VOs), where VOs generate revenue from providing both the premium Wi-Fi access and the advertising sponsored Wi-Fi access to mobile users (MUs). With the premium access, MUs directly pay VOs for their Wi-Fi usage; while with the advertising sponsored access, MUs watch advertisements for the free usage of Wi-Fi. VOs sell their ad spaces to advertisers (ADs) via an ad platform, and share a proportion of the revenue with the ad platform. We formulate the economic interactions among the ad platform, VOs, MUs, and ADs as a three-stage Stackelberg game. By analyzing the equilibrium, we show that the ad platform's advertising revenue sharing policy affects a VO's Wi-Fi price but not the VO's advertising price. Moreover, we prove that a single term called equilibrium indicator determines whether a VO will fully rely on the premium access, or fully rely on the advertising sponsored access, or obtain revenue from both types of access. Numerical results show that the VO obtains a large revenue under a large advertising concentration level and a medium MU visiting frequency.

Motivated by the recent efforts in extending LTE to the unlicensed spectrum, we propose a novel spectrum sharing framework for the coopetition (i.e., cooperation and competition) between LTE and Wi-Fi in the unlicensed band. Basically, the LTE network chooses to work in one of the two modes: in the competition mode, it randomly accesses an unlicensed channel, and interferes with a Wi-Fi access point; in the cooperation mode, it onloads a Wi-Fi access point's traffic in exchange for the full access of the corresponding channel. Because the LTE network works in an interference-free manner in the cooperation mode, it can achieve a much larger total data rate (comparing to the competition mode) to serve both its own users and the Wi-Fi users under proper channel conditions. To achieve the maximum potential of this novel coopetition framework, we design a reverse auction mechanism, where the LTE provider is the auctioneer (buyer), and the Wi-Fi access point owners (APOs) are the bidders who compete to sell their channels to the LTE provider. An APO's bid indicates the data rate that it would like the LTE provider to offer in the cooperation mode. We show that the auction involves the allocative externalities, i.e., the cooperation between the LTE provider and an APO benefits other APOs who are not directly involved in this cooperation. As a result, a particular APO's bidding strategy is affected by its belief about other APOs' bidding strategies. This makes our analysis much more challenging than that of the standard second-price auction, where bidding truthfully is a weakly dominant strategy. We characterize the APOs' unique equilibrium bidding strategies, and analyze the LTE provider's optimal reserve rate that maximizes its payoff for a general APO type distribution. Our analysis shows that only when the LTE throughput exceeds a threshold, the LTE provider will choose a reasonably large reserve rate to cooperate with the APOs; otherwise, it will restrict the reserve rate to a small value and work in the competition mode.

The explosive growth of global mobile traffic has led to rapid growth in the energy consumption in communication networks. In this paper, we focus on the energy-aware design of the network selection, subchannel, and power allocation in cellular and Wi-Fi networks, while taking into account the traffic delay of mobile users. Based on the two-timescale Lyapunov optimization technique, we first design an online Energy-Aware Network Selection and Resource Allocation (ENSRA) algorithm, which yields a power consumption within$O\left({\frac{1}{V}} \right)$bound of the optimal value, and guarantees an$O\left(V \right)$traffic delay for any positive control parameter$V$. Motivated by the recent advancement in the accurate estimation and prediction of user mobility, channel conditions, and traffic demands, we further develop a novel predictive Lyapunov optimization technique to utilize the predictive information, and propose a Predictive Energy-Aware Network Selection and Resource Allocation (P-ENSRA) algorithm. We characterize the performance bounds of P-ENSRA in terms of the power-delay tradeoff theoretically. To reduce the computational complexity, we finally propose a Greedy Predictive Energy-Aware Network Selection and Resource Allocation (GP-ENSRA) algorithm, where the operator solves the problem in P-ENSRA approximately and iteratively. Numerical results show that GP-ENSRA significantly improves the power-delay performance over ENSRA in the large delay regime. For a wide range of system parameters, GP-ENSRA reduces the traffic delay over ENSRA by 20–30% under the same power consumption.

In this paper, we study the cooperative Wi-Fi deployment problem, where the mobile network operator (MNO) cooperates with some venue owners (VOs) to deploy public Wi-Fi networks. The MNO negotiates with the VOs to determine where to deploy Wi-Fi and how much to pay. The MNO's objective is to maximize its payoff, which depends on the payments to VOs, the benefits due to data offloading and mobile advertising, and the costs due to deploying and operating Wi-Fi. We analyze the interactions among the MNO and VOs under the one-to-many bargaining framework, where the MNO bargains with VOs sequentially, taking into account the externalities among different steps of bargaining. We apply the Nash bargaining theory to analyze the cases with exogenous and endogenous bargaining sequences. For the former case, the bargaining sequence is predetermined, and we apply backward induction to compute the optimal bargaining solution related to the cooperation decisions and payments. For the latter case, the MNO can decide the bargaining sequence to maximize its payoff. We explore the structural property of the one-to-many bargaining, and design an Optimal VO Bargaining Sequencing (OVBS) algorithm that computes the optimal bargaining sequence. More precisely, we categorize VOs into three types based on the impact of the Wi-Fi deployment at their venues, and show that it is optimal for the MNO to bargain with these three types of VOs sequentially. Numerical results show that the optimal bargaining sequence improves the MNO's payoff over the random and worst bargaining sequences by up to 14.7% and 45.8%, respectively.

To address the increasingly severe congestion problem in cellular networks, mobile operators are actively considering offloading the cellular traffic to other complementary networks. In this paper, we study the online network selection problem in operator-initiated data offloading with multiple mobile users, taking into account the operation cost, queueing delay, and traffic load in different access networks (e.g., cellular macrocell, femtocell, and Wi-Fi networks). We first design a Delay-Aware Network Selection (DNS) algorithm based on the Lyapunov optimization technique. The DNS algorithm yields an operation cost within O (1/V) bound of the optimal value, and guarantees an O (V) traffic delay for any control parameter V > 0. Next, we incorporate the prediction of users' mobilities and traffic arrivals into the network selection. Specifically, we assume that the users' locations and traffic arrivals in the next few time slots can be estimated accurately, and propose a Predictive Delay-Aware Network Selection (P-DNS) algorithm to utilize this information based on a novel frame-based design. We characterize the performance bounds of P-DNS in terms of cost-delay tradeoff theoretically. To further reduce the computational complexity, we propose a Greedy Predictive Delay-Aware Network Selection (GP-DNS) algorithm, where the operator solves the network selection problem approximately and iteratively. Numerical results show that GP-DNS improves the cost-delay performance over DNS, and reduces the queueing delay by roughly 40% with the same operation cost.